x<-c("car", "lme4", "ggplot2", "dplyr", "tibble", "stringr", 'fitdistrplus', 'sjPlot', "condformat", 'kableExtra')
lapply(x, require, character.only = TRUE)
GC<-read.csv("GCunmatched.csv", h=T)
NEO<-read.csv("NEOunmatched.csv", h=T)
GC_Behavior<-read.csv("GC_behavior_matched.csv")
TL_NEO<-read.csv("Time-lagged_NEO.csv", h=T)
Concurrent_NEO<-read.csv("Concurrent_NEO.csv", h=T)
par(mfrow=c(1,2))
hist(GC$final.conc, breaks=100)
hist(log(GC$final.conc),breaks=100)
ln transformed fGC more normally distributed
descdist(log(GC$final.conc), boot= 500)
## summary statistics
## ------
## min: 4.817051 max: 8.023356
## median: 6.507725
## mean: 6.539997
## estimated sd: 0.5080615
## estimated skewness: 0.04898005
## estimated kurtosis: 3.512847
plotdist(log(GC$final.conc), histo=TRUE, demp=TRUE)
par(mfrow=c(1,2))
hist(NEO$NEO.CR, breaks=100)
hist((log(NEO$NEO.CR)), breaks=100)
ln transformed NEO more normally distributed
descdist(log(NEO$NEO.CR), boot= 500)
## summary statistics
## ------
## min: 3.659753 max: 6.855729
## median: 5.42101
## mean: 5.430162
## estimated sd: 0.4942345
## estimated skewness: 0.1014
## estimated kurtosis: 2.873707
plotdist(log(NEO$NEO.CR), histo=TRUE, demp=TRUE)
GC.LM1=lm(log(GC.final.conc)~ FreqAllProx + RelRank, data=GC_Behavior)
GC.LM2=lm(log(GC.final.conc)~ FreqTotalGR + RelRank, data=GC_Behavior)
GC.LM3=lm(log(GC.final.conc)~ FreqGRRec + RelRank, data=GC_Behavior)
GC.LM4=lm(log(GC.final.conc)~ FreqGRGiven + RelRank, data=GC_Behavior)
GC.LM5=lm(log(GC.final.conc)~ FreqTimeSpentCop + RelRank, data=GC_Behavior)
GC.LM6=lm(log(GC.final.conc)~ FreqTimeSpentConsort + RelRank, data=GC_Behavior)
par(mfrow = c(2,2))
GC.LM1=lm(log(GC.final.conc)~ FreqAllProx + RelRank, data=GC_Behavior)
plot(GC.LM1)
GC.LM2=lm(log(GC.final.conc)~ FreqTotalGR + RelRank, data=GC_Behavior)
plot(GC.LM2)
GC.LM3=lm(log(GC.final.conc)~ FreqGRRec + RelRank, data=GC_Behavior)
plot(GC.LM3)
GC.LM4=lm(log(GC.final.conc)~ FreqGRGiven + RelRank, data=GC_Behavior)
plot(GC.LM4)
GC.LM5=lm(log(GC.final.conc)~ FreqTimeSpentCop + RelRank, data=GC_Behavior)
plot(GC.LM5)
GC.LM6=lm(log(GC.final.conc)~ FreqTimeSpentConsort + RelRank, data=GC_Behavior)
plot(GC.LM6)
points appear evenly disbursed around 0
NEO.TL.LM1=lm(log(NEO.CR)~ FreqAllProx + RelRank, data=TL_NEO)
NEO.TL.LM2=lm(log(NEO.CR)~ FreqTotalGR + RelRank, data=TL_NEO)
NEO.TL.LM3=lm(log(NEO.CR)~ FreqGRRec + RelRank, data=TL_NEO)
NEO.TL.LM4=lm(log(NEO.CR)~ FreqGRGiven + RelRank, data=TL_NEO)
NEO.TL.LM5=lm(log(NEO.CR)~ FreqTimeSpentCop + RelRank, data=TL_NEO)
NEO.TL.LM6=lm(log(NEO.CR)~ FreqTimeSpentConsort + RelRank, data=TL_NEO)
par(mfrow = c(2,2))
NEO.TL.LM1=lm(log(NEO.CR)~ FreqAllProx + RelRank, data=TL_NEO)
plot(NEO.TL.LM1)
NEO.TL.LM2=lm(log(NEO.CR)~ FreqTotalGR + RelRank, data=TL_NEO)
plot(NEO.TL.LM2)
NEO.TL.LM3=lm(log(NEO.CR)~ FreqGRRec + RelRank, data=TL_NEO)
plot(NEO.TL.LM3)
NEO.TL.LM4=lm(log(NEO.CR)~ FreqGRGiven + RelRank, data=TL_NEO)
plot(NEO.TL.LM4)
NEO.TL.LM5=lm(log(NEO.CR)~ FreqTimeSpentCop + RelRank, data=TL_NEO)
plot(NEO.TL.LM5)
NEO.TL.LM6=lm(log(NEO.CR)~ FreqTimeSpentConsort + RelRank, data=TL_NEO)
plot(NEO.TL.LM6)
points appear evenly disbursed around 0
NEO.Con.LM1=lm(log(NEO.CR)~ FreqAllProx + RelRank, data=Concurrent_NEO)
NEO.Con.LM2=lm(log(NEO.CR)~ FreqTotalGR + RelRank, data=Concurrent_NEO)
NEO.Con.LM3=lm(log(NEO.CR)~ FreqGRRec + RelRank, data=Concurrent_NEO)
NEO.Con.LM4=lm(log(NEO.CR)~ FreqGRGiven + RelRank, data=Concurrent_NEO)
NEO.Con.LM5=lm(log(NEO.CR)~ FreqTimeSpentCop + RelRank, data=Concurrent_NEO)
NEO.Con.LM6=lm(log(NEO.CR)~ FreqTimeSpentConsort + RelRank, data=Concurrent_NEO)
par(mfrow = c(2,2))
NEO.Con.LM1=lm(log(NEO.CR)~ FreqAllProx + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM1)
NEO.Con.LM2=lm(log(NEO.CR)~ FreqTotalGR + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM2)
NEO.Con.LM3=lm(log(NEO.CR)~ FreqGRRec + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM3)
NEO.Con.LM4=lm(log(NEO.CR)~ FreqGRGiven + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM4)
NEO.Con.LM5=lm(log(NEO.CR)~ FreqTimeSpentCop + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM5)
NEO.Con.LM6=lm(log(NEO.CR)~ FreqTimeSpentConsort + RelRank, data=Concurrent_NEO)
plot(NEO.Con.LM6)
residuals appear evenly disbursed around 0
| GC_Linear_model | Independent_Variable | VIF |
|---|---|---|
| model 1 | FreqAllProx | 1.070511 |
| model 2 | FreqTotalGR | 1.020731 |
| model 3 | FreqGRRec | 1.016083 |
| model 4 | FreqGRGiven | 1.006063 |
| model 5 | FreqTimeSpentCop | 1.053784 |
| model 6 | FreqTimeSpentConsort | 1.019914 |
All VIFs < 2
| NEO.TL_Linear_model | Independent_Variable | VIF |
|---|---|---|
| model 1 | FreqAllProx | 1.068882 |
| model 2 | FreqTotalGR | 1.002498 |
| model 3 | FreqGRRec | 1.004169 |
| model 4 | FreqGRGiven | 1.000119 |
| model 5 | FreqTimeSpentCop | 1.031555 |
| model 6 | FreqTimeSpentConsort | 1.049770 |
All VIFs < 2
| NEO.Con_Linear_model | Independent_Variable | VIF |
|---|---|---|
| model 1 | FreqAllProx | 1.067799 |
| model 2 | FreqTotalGR | 1.000007 |
| model 3 | FreqGRRec | 1.000234 |
| model 4 | FreqGRGiven | 1.000196 |
| model 5 | FreqTimeSpentCop | 1.026227 |
| model 6 | FreqTimeSpentConsort | 1.051667 |
All VIFs < 2